000 | 03601nam a22005175i 4500 | ||
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001 | 978-3-658-16756-1 | ||
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007 | cr nn 008mamaa | ||
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_a9783658167561 _9978-3-658-16756-1 |
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024 | 7 |
_a10.1007/978-3-658-16756-1 _2doi |
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050 | 4 | _aTJ212-225 | |
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_a629.8312 _223 |
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100 | 1 |
_aChen, Zhiwen. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _953869 |
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245 | 1 | 0 |
_aData-Driven Fault Detection for Industrial Processes _h[electronic resource] : _bCanonical Correlation Analysis and Projection Based Methods / _cby Zhiwen Chen. |
250 | _a1st ed. 2017. | ||
264 | 1 |
_aWiesbaden : _bSpringer Fachmedien Wiesbaden : _bImprint: Springer Vieweg, _c2017. |
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300 |
_aXIX, 112 p. 39 illus. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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505 | 0 | _aA New Index for Performance Evaluation of FD Methods -- CCA-based FD Method for the Monitoring of Stationary Processes -- Projection-based FD Method for the Monitoring of Dynamic Processes -- Benchmark Study and Real-Time Implementation. . | |
520 | _aZhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed. Contents A New Index for Performance Evaluation of FD Methods CCA-based FD Method for the Monitoring of Stationary Processes Projection-based FD Method for the Monitoring of Dynamic Processes Benchmark Study and Real-Time Implementation Target Groups Researchers and students in the field of process control and statistical hypothesis testing Research and development engineers in the process industry About the Author Zhiwen Chen’s research interests include multivariate statistical process monitoring, model-based and data-driven fault diagnosis as well as their application to industrial processes. He is currently working at the School of Information Science and Engineering at Central South University, China. | ||
650 | 0 |
_aControl engineering. _931970 |
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650 | 0 |
_aEngineering mathematics. _93254 |
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650 | 0 |
_aEngineering—Data processing. _931556 |
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650 | 1 | 4 |
_aControl and Systems Theory. _931972 |
650 | 2 | 4 |
_aMathematical and Computational Engineering Applications. _931559 |
710 | 2 |
_aSpringerLink (Online service) _953870 |
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773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783658167554 |
776 | 0 | 8 |
_iPrinted edition: _z9783658167578 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-658-16756-1 |
912 | _aZDB-2-ENG | ||
912 | _aZDB-2-SXE | ||
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